Quickest detection in practice in presence of seasonality: an illustration with call center data
| Autor: | Patrick Laub, Nicole El Karoui, Stéphane Loisel, Yahia Salhi |
|---|---|
| Přispěvatelé: | Laboratoire de Sciences Actuarielle et Financière (SAF), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Loisel, Stéphane |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] [MATH.MATH-PR] Mathematics [math]/Probability [math.PR] [QFIN.RM] Quantitative Finance [q-fin]/Risk Management [q-fin.RM] Applications (stat.AP) [QFIN.RM]Quantitative Finance [q-fin]/Risk Management [q-fin.RM] [SHS.ECO] Humanities and Social Sciences/Economics and Finance [SHS.ECO]Humanities and Social Sciences/Economics and Finance Statistics - Applications Statistics - Computation Computation (stat.CO) ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Data analytics and Models for Insurance Data analytics and Models for Insurance, 2020 HAL |
| Popis: | In this chapter, we explain how quickest detection algorithms can be useful for risk management in presence of seasonality. We investigate the problem of detecting fast enough cases when a call center will need extra staff in a near future with a high probability. We illustrate our findings on real data provided by a French insurer. We also discuss the relevance of the CUSUM algorithm and of some machine-learning type competitor for this applied problem. |
| Databáze: | OpenAIRE |
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